Model profile

MITRA

MITRA is a pretrained model for tabular classification and regression that learns from a mixture of synthetic datasets generated by different rules. It uses labelled examples as context for new predictions, and AutoGluon's implementation also adapts its pretrained weights to the target dataset through fine-tuning.

Tabular FoundationRegular feature limit: 500Source ↗

Model profile

MITRA

Default parameters. Peer models use their prespecified benchmark-default configuration without per-dataset tuning. This card describes that configuration, not the best achievable tuned model.

Operating map

Macro-F1 Elo relative to Random Forest. Columns are training-sample caps; rows are dataset modalities. Small labels show the number of binding targets.

Elo difference vs. Random Forest

Fixed nonlinear scale; extremes capped at ±1,000 Elo.

No result / comparison unavailable

Reliability

Failure rate map

Dataset modality

Failed fits / (successful + failed fits) for the selected model and modality. Design skips are omitted. A dash means no attempted fits; 0% means all attempted fits succeeded. Hover for counts. Colours span 0–20%, with rates above 20% using the darkest shade; cell labels and hover retain the actual rate. This chart is independent of the performance sensitivity view.

Reference benchmark results

Strict Macro-F1 Elo at p=10,000, n=100. Random Forest is anchored at 1,000; intervals are target-bootstrap 95% intervals. These reference results stay fixed while you explore other budgets in the charts. Overlapping intervals do not establish a clear winner.

Dataset modality	Elo	95% interval	Targets
All modalities	962	913–1,012	44
DNA methylation	997	760–1,273	4
GLM/PLM Embeddings	1,083	763–1,466	4
Gene expression	964	867–1,046	10
Genomic prediction	660	478–743	4
Metagenomics	989	927–1,047	10
Molecular properties	957	887–1,031	10
Other	1,000	993–1,007	2